library(ggplot2)
library(gridExtra)
library(dplyr)
library(RColorBrewer)
midA = read.csv(file="./data/MID/MIDA_4.01.csv", sep= ",")
midB = read.csv(file="./data/MID/MIDB_4.01.csv", sep= ",")
midI = read.csv(file="./data/MID/MIDI_4.01.csv", sep= ",")
midIP = read.csv(file="./data/MID/MIDIP_4.01.csv", sep= ",")
## Combine A&B 
midAB = merge(x = midB, y = midA, by = "DispNum3", all.x = TRUE)
midAB <- midAB[,c(1,3,  4,  7,  10, 11, 15, 16, 17, 18, 19, 21, 28, 29, 32, 33, 36, 37, 38, 39, 40, 41, 42)]
midAB <- midAB[midAB$EndYear.x >= 1900,]
midA <- midA[midA$EndYear>= 1900,]
midB <- midB[midB$EndYear>= 1900,]
midAB$Outcome <- as.factor(midAB$Outcome)
midAB$HiAct.x <- as.factor(midAB$HiAct.x)
d=data.frame(x1=c(1914,1939, 1947, 1950, 1955, 2001), x2=c(1918, 1945, 1991, 1953, 1975, 2010), y1=c(0,0,0,0,0,0), Conflict=c("WWI", "WWII", "Cold War", "Korean War", "Vietnam War", "Afghanistan War"), r=c(1,2,3,4,5,6))
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=250, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) 

Facet by Outcome

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=175, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~Outcome)

Facet by Highest Action in Dispute

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=150, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~HiAct)

Facet by Hostility Level of Dispute

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=200, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~HostLev)

Facet by Settlement of Dispute

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=200, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~Settle)

US Disputes

us <- filter(midB, ccode%in%2)
us = merge(x = us, y = midA, by = "DispNum3", all.x = TRUE)
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~Outcome)

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~Orig)

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~HostLev.x)

World War I

WWI ————————– 1914-1918
WWII ————————- 1939-1945
Cold War ——————– 1947-1991
Korean War —————- 1950-1953
Vietnam War ————— 1955-1975
War in Afghanistan ——- 2001-2010

d=data.frame(x1=c(1914,1939, 1947, 1950, 1955, 2001), x2=c(1918, 1945, 1991, 1953, 1975, 2010), y1=c(0,0,0,0,0,0), Conflict=c("WWI", "WWII", "Cold War", "Korean War", "Vietnam War", "Afghanistan War"), r=c(1,2,3,4,5,6))
WWI <- midAB
WWI <- midAB[midAB$StYear.x >= 1909,]
WWI <- midAB[midAB$StYear.x <= 1925,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1914, xmax=1918, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWI, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1914, xmax=1918, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWI, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

World War II

WWII <- midAB
WWII <- WWII[WWII$StYear.x >= 1930,]
WWII <- WWII[WWII$StYear.x <= 1950,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWII, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWII, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin=0, ymax=150),alpha=0.15, fill = "gray" ) +
geom_histogram(data = WWII, aes(WWII$StYear.x)) 

Cold War

coldwar <- midAB
coldwar <- coldwar[midAB$StYear.x >= 1940,]
coldwar <- coldwar[midAB$StYear.x <= 1999,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1947, xmax=1991, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = coldwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1947, xmax=1991, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = coldwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin=0, ymax=150),alpha=0.15, fill = "gray" ) +
geom_histogram(data = WWII, aes(WWII$StYear.x)) 

Korean War

kwar <- midAB
kwar <- midAB[midAB$StYear.x >= 1947,]
kwar <- midAB[midAB$StYear.x <= 1955,]
vwar <- midAB
vwar <- midAB[midAB$StYear.x >= 1950,]
vwar <- midAB[midAB$StYear.x <= 1980,]
awar <- midAB
awar <- midAB[midAB$StYear.x >= 1996,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1950, xmax=1953, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = kwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1950, xmax=1953, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = kwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

Vietnam War

vwar <- midAB
vwar <- midAB[midAB$StYear.x >= 1950,]
vwar <- midAB[midAB$StYear.x <= 1980,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1955, xmax=1975, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = vwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1955, xmax=1975, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = vwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

Afghanistan War

awar <- midAB
awar <- midAB[midAB$StYear.x >= 1996,]
a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=2001, xmax=2010, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = awar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)
b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=2001, xmax=2010, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = awar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)
grid.arrange(a,b, nrow =2)

---
title: "MID"
output: html_notebook
---

```{r fig.width=10, fig.height=6}
library(ggplot2)
library(gridExtra)
library(dplyr)
library(RColorBrewer)

midA = read.csv(file="./data/MID/MIDA_4.01.csv", sep= ",")
midB = read.csv(file="./data/MID/MIDB_4.01.csv", sep= ",")
midI = read.csv(file="./data/MID/MIDI_4.01.csv", sep= ",")
midIP = read.csv(file="./data/MID/MIDIP_4.01.csv", sep= ",")

## Combine A&B 

midAB = merge(x = midB, y = midA, by = "DispNum3", all.x = TRUE)

midAB <- midAB[,c(1,3,	4,	7,	10,	11,	15,	16,	17,	18,	19,	21,	28,	29,	32,	33,	36,	37,	38,	39,	40,	41,	42)]
midAB <- midAB[midAB$EndYear.x >= 1900,]
midA <- midA[midA$EndYear>= 1900,]
midB <- midB[midB$EndYear>= 1900,]

midAB$Outcome <- as.factor(midAB$Outcome)
midAB$HiAct.x <- as.factor(midAB$HiAct.x)

d=data.frame(x1=c(1914,1939, 1947, 1950, 1955, 2001), x2=c(1918, 1945, 1991, 1953, 1975, 2010), y1=c(0,0,0,0,0,0), Conflict=c("WWI", "WWII", "Cold War", "Korean War", "Vietnam War", "Afghanistan War"), r=c(1,2,3,4,5,6))

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=250, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) 



```

## Facet by Outcome
```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=175, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~Outcome)

```


## Facet by Highest Action in Dispute 

```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=150, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~HiAct)

```


## Facet by Hostility Level of Dispute 
```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=200, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~HostLev)

```


## Facet by Settlement of Dispute 
```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=200, fill = Conflict ),alpha=0.5) +
geom_histogram(data = midA, aes(midA$StYear), alpha = .4) + facet_wrap(~Settle)

```



# US Disputes 

```{r fig.width=15, fig.height=10}
us <- filter(midB, ccode%in%2)

us = merge(x = us, y = midA, by = "DispNum3", all.x = TRUE)

ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~Outcome)

```


```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~Orig)

```


```{r fig.width=15, fig.height=10}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin = x1, xmax=x2, ymin=0, ymax=30, fill = Conflict ),alpha=0.3) +
geom_histogram(data = us , aes(us$StYear.x), alpha = .4) + facet_wrap(~HostLev.x)

```









## World War I 

WWI    -------------------------- 1914-1918  
WWII  ------------------------- 1939-1945  
Cold War -------------------- 1947-1991  
Korean War ---------------- 1950-1953  
Vietnam War --------------- 1955-1975  
War in Afghanistan ------- 2001-2010 



```{r fig.width=10, fig.height=14}

d=data.frame(x1=c(1914,1939, 1947, 1950, 1955, 2001), x2=c(1918, 1945, 1991, 1953, 1975, 2010), y1=c(0,0,0,0,0,0), Conflict=c("WWI", "WWII", "Cold War", "Korean War", "Vietnam War", "Afghanistan War"), r=c(1,2,3,4,5,6))

WWI <- midAB
WWI <- midAB[midAB$StYear.x >= 1909,]
WWI <- midAB[midAB$StYear.x <= 1925,]


a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1914, xmax=1918, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWI, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1914, xmax=1918, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWI, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)

```

## World War II
```{r fig.width=10, fig.height=14}
WWII <- midAB
WWII <- WWII[WWII$StYear.x >= 1930,]
WWII <- WWII[WWII$StYear.x <= 1950,]


a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWII, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = WWII, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)
```

```{r fig.width=10, fig.height=14}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin=0, ymax=150),alpha=0.15, fill = "gray" ) +
geom_histogram(data = WWII, aes(WWII$StYear.x)) 


```





## Cold War 
```{r fig.width=15, fig.height=20}
coldwar <- midAB
coldwar <- coldwar[midAB$StYear.x >= 1940,]
coldwar <- coldwar[midAB$StYear.x <= 1999,]

a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1947, xmax=1991, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = coldwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1947, xmax=1991, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = coldwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)
```



```{r}
ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1939, xmax=1945, ymin=0, ymax=150),alpha=0.15, fill = "gray" ) +
geom_histogram(data = WWII, aes(WWII$StYear.x)) 

```


##Korean War 
```{r fig.width=10, fig.height=14}
kwar <- midAB
kwar <- midAB[midAB$StYear.x >= 1947,]
kwar <- midAB[midAB$StYear.x <= 1955,]

vwar <- midAB
vwar <- midAB[midAB$StYear.x >= 1950,]
vwar <- midAB[midAB$StYear.x <= 1980,]

awar <- midAB
awar <- midAB[midAB$StYear.x >= 1996,]


a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1950, xmax=1953, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = kwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1950, xmax=1953, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = kwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)
```


## Vietnam War
```{r fig.width=10, fig.height=14}

vwar <- midAB
vwar <- midAB[midAB$StYear.x >= 1950,]
vwar <- midAB[midAB$StYear.x <= 1980,]


a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1955, xmax=1975, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = vwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=1955, xmax=1975, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = vwar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)
```


# Afghanistan War 
```{r fig.width=10, fig.height=14}

awar <- midAB
awar <- midAB[midAB$StYear.x >= 1996,]


a <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=2001, xmax=2010, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = awar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~SideA)

b <- ggplot() + 
geom_rect(data=d, mapping=aes(xmin=2001, xmax=2010, ymin="AAB", ymax="ZIM"),alpha=0.15, fill = "salmon" ) +
geom_point(data = awar, aes(x = StYear.x, y = StAbb, color = Outcome)) + facet_wrap(~Orig)

grid.arrange(a,b, nrow =2)
```



